Instructions to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000")
model = AutoModelForCausalLM.from_pretrained("ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000

SGLang

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000 with Docker Model Runner:
```
docker model run hf.co/ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_6000
```

ccui46 commited on Apr 14

Commit

01e80bc

verified ·

1 Parent(s): 8cd478e

Add loss curve + metrics @ step 6000

Browse files

Files changed (2) hide show

loss_curve.png +0 -0
metrics.json +8 -0

loss_curve.png ADDED Viewed

metrics.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+  "step": 6000,
+  "eval_loss_at_step": 0.5764480829238892,
+  "train_loss_last_logged": 0.008691122382879257,
+  "eval_loss_last_logged": 0.5764480829238892,
+  "num_train_logs": 600,
+  "num_eval_logs": 1
+}